Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2024 Oct 23;14(1):25006.
doi: 10.1038/s41598-024-75876-2.

Automating cancer diagnosis using advanced deep learning techniques for multi-cancer image classification

Yogesh Kumar  1 , Supriya Shrivastav  2 , Kinny Garg  3 , Nandini Modi  4 , Katarzyna Wiltos  5 , Marcin Woźniak  6 , Muhammad Fazal Ijaz  7
Affiliations

Automating cancer diagnosis using advanced deep learning techniques for multi-cancer image classification

Yogesh Kumar et al. Sci Rep. .

Abstract

Cancer detection poses a significant challenge for researchers and clinical experts due to its status as the leading cause of global mortality. Early detection is crucial, but traditional cancer detection methods often rely on invasive procedures and time-consuming analyses, creating a demand for more efficient and accurate solutions. This paper addresses these challenges by utilizing automated cancer detection through AI-based techniques, specifically focusing on deep learning models. Convolutional Neural Networks (CNNs), including DenseNet121, DenseNet201, Xception, InceptionV3, MobileNetV2, NASNetLarge, NASNetMobile, InceptionResNetV2, VGG19, and ResNet152V2, are evaluated on image datasets for seven types of cancer: brain, oral, breast, kidney, Acute Lymphocytic Leukemia, lung and colon, and cervical cancer. Initially, images undergo segmentation techniques, proceeded by contour feature extraction where parameters such as perimeter, area, and epsilon are computed. The models are rigorously evaluated, with DenseNet121 achieving the highest validation accuracy as 99.94%, 0.0017 as loss, and the lowest Root Mean Square Error (RMSE) values as 0.036056 for training and 0.045826 for validation. These results revealed the capability of AI-based techniques in improving cancer detection accuracy, with DenseNet121 emerging as the most effective model in this study.

Keywords: Cancer Detection; Deep learning; Medical imaging; Multi-cancer diagnosis; Noise removal; Radiation Therapy.

PubMed Disclaimer

Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Proposed system design for Multi-Cancer classification.
Fig. 2
Fig. 2
Segmentation techniques on different classes of cancer.
Fig. 3
Fig. 3
RMSE Values of Classifiers Applied to the Multi-Cancer Dataset.
Fig. 4
Fig. 4
Training and Validation Learning Curves of the Classifiers on the Multi-Cancer Dataset.
Fig. 4
Fig. 4
Training and Validation Learning Curves of the Classifiers on the Multi-Cancer Dataset.
Fig. 4
Fig. 4
Training and Validation Learning Curves of the Classifiers on the Multi-Cancer Dataset.
Fig. 5
Fig. 5
RMSE values of classifiers for different classes of Multi-Cancer data.
Fig. 6
Fig. 6
Training time of applied classifiers for Multi-Cancer dataset.
Fig. 7
Fig. 7
Prediction evaluation of classifiers for different classes of Multi-Cancer dataset.
See this image and copyright information in PMC

References

    1. Hesso, I. et al. Cancer care at the time of the fourth industrial revolution: an insight to healthcare professionals’ perspectives on cancer care and artificial intelligence. Radiat. Oncol.18(1), 167 (2023). - PMC - PubMed
    1. Mansoori, B., Mohammadi, A., Davudian, S., Shirjang, S. & Baradaran, B. The different mechanisms of cancer drug resistance: a brief review. Adv. Pharm. Bull.7(3), 339 (2017). - PMC - PubMed
    1. Cancer Facts & Fig. 2023. American Cancer Society. (2023). https://www.cancer.org/research/cancer-facts-statistics/all-cancer-facts...
    1. Tufail, A. B. et al. Deep learning in cancer diagnosis and prognosis prediction: a minireview on challenges, recent trends, and future directions. Computational and Mathematical Methods in Medicine, 2021. (2021). - PMC - PubMed
    1. Debela, D. T. et al. New approaches and procedures for cancer treatment:Current perspectives. SAGE open medicine, 9, 20503121211034366 (2021). - PMC - PubMed

MeSH terms

LinkOut - more resources